Observability

Spring AI builds upon the observability features in the Spring ecosystem to provide insights into AI-related operations. Spring AI provides metrics and tracing capabilities for its core components: ChatClient (including Advisors), ChatModel, EmbeddingModel, ImageModel and VectorStore.

Low cardinality keys will be added to metrics and traces, while high cardinality keys will only be added to traces.

ChatClient

Table 1. Low cardinality Keys
Name	Description
`gen_ai.operation.name`	`framework`
`gen_ai.system`	`spring_ai`
`spring.ai.kind`	Spring AI kind - `chat_client`
`spring.ai.chat.client.stream`	Is the chat model response a stream - `true or false`

Table 2. High cardinality Keys
Name	Description
`spring.ai.chat.client.advisor.params`	Map of advisor parameters.
`spring.ai.chat.client.advisors`	List of configured chat client advisors.
`spring.ai.chat.client.stream`	Is the chat model response a stream.
`spring.ai.chat.client.system.params`	Chat client system parameters.
`spring.ai.chat.client.system.text`	Chat client system text.
`spring.ai.chat.client.tool.function.names`	Enabled tool function names.
`spring.ai.chat.client.tool.functioncallbacks`	List of configured chat client function callbacks.
`spring.ai.chat.client.user.params`	Chat client user parameters.
`spring.ai.chat.client.user.text`	Chat client user text.

ChatClient input data

The ChatClient input data is typically too big to be included in an observation as span attributes. The preferred way to store large data it is as span events, which are supported by OpenTelemetry but not yet surfaced through the Micrometer APIs. Spring AI supports storing these fields as events in OpenTelemetry and will provide a more general event based solution once the issue github.com/micrometer-metrics/micrometer/issues/5238 is resolved.

Property

Description

Default

spring.ai.chat.client.observations.include-input

Enables the inclusion of the input content in the observations. This brings the risk of exposing sensitive or private information. Please, be careful!

false

ChatClient Advisors

Table 3. Low Cardinality Keys
Name	Description
`spring.ai.kind`	Spring AI kind - `chat_client_advisor`
`spring.ai.chat.client.advisor.type`	Where the advisor applies it’s logic in the request processing, one of `BEFORE`, `AFTER`, or `AROUND`.

Table 4. High Cardinality Keys
Name	Description
`spring.ai.chat.client.advisor.name`	Name of the advisor

ChatModel

Observability features are currently supported only for ChatModel and EmbeddingModel implementations from the following AI model providers: OpenAI, Ollama, Anthropic, and Mistral. Additional AI model providers will be supported in a future release.

Table 5. Low Cardinality Keys
Name	Description
`gen_ai.operation.name`	The name of the operation being performed.
`gen_ai.system`	The model provider as identified by the client instrumentation.
`gen_ai.request.model`	The name of the model a request is being made to.
`gen_ai.response.model`	The name of the model that generated the response.

Table 6. High Cardinality Keys
Name	Description
`gen_ai.request.frequency_penalty`	The frequency penalty setting for the model request.
`gen_ai.request.max_tokens`	The maximum number of tokens the model generates for a request.
`gen_ai.request.presence_penalty`	The presence penalty setting for the model request.
`gen_ai.request.stop_sequences`	List of sequences that the model will use to stop generating further tokens.
`gen_ai.request.temperature`	The temperature setting for the model request.
`gen_ai.request.top_k`	The top_k sampling setting for the model request.
`gen_ai.request.top_p`	The top_p sampling setting for the model request.
`gen_ai.response.finish_reasons`	Reasons the model stopped generating tokens, corresponding to each generation received.
`gen_ai.response.id`	The unique identifier for the AI response.
`gen_ai.usage.input_tokens`	The number of tokens used in the model input (prompt).
`gen_ai.usage.output_tokens`	The number of tokens used in the model output (completion).
`gen_ai.usage.total_tokens`	The total number of tokens used in the model exchange.
`gen_ai.prompt`	The full prompt sent to the model.
`gen_ai.completion`	The full response received from the model.

Chat prompt and completion data

The chat prompt and completion data are typically too big to be included in an observation as span attributes. The preferred way to store large data it is as span events, which are supported by OpenTelemetry but not yet surfaced through the Micrometer APIs. Spring AI supports storing these fields as events in OpenTelemetry and will provide a more general event based solution once the issue github.com/micrometer-metrics/micrometer/issues/5238 is resolved.

Property

Description

Default

spring.ai.chat.observations.include-prompt

true or false

false

spring.ai.chat.observations.include-completion

true or false

false

EmbeddingModel

Table 7. Low Cardinality Keys
Name	Description
`gen_ai.operation.name`	The name of the operation being performed.
`gen_ai.system`	The model provider as identified by the client instrumentation.
`gen_ai.request.model`	The name of the model a request is being made to.
`gen_ai.response.model`	The name of the model that generated the response.

Table 8. High Cardinality Keys
Name	Description
`gen_ai.request.embedding.dimensions`	The number of dimensions the resulting output embeddings have.
`gen_ai.usage.input_tokens`	The number of tokens used in the model input.
`gen_ai.usage.total_tokens`	The total number of tokens used in the model exchange.

ImageModel

Table 9. Low Cardinality Keys
Name	Description
`gen_ai.operation.name`	The name of the operation being performed.
`gen_ai.system`	The model provider as identified by the client instrumentation.
`gen_ai.request.model`	The name of the model a request is being made to.

Table 10. High Cardinality Keys
Name	Description
`gen_ai.request.image.response_format`	The format in which the generated image is returned.
`gen_ai.request.image.size`	The size of the image to generate.
`gen_ai.request.image.style`	The style of the image to generate.
`gen_ai.response.id`	The unique identifier for the AI response.
`gen_ai.response.model`	The name of the model that generated the response.
`gen_ai.usage.input_tokens`	The number of tokens used in the model input (prompt).
`gen_ai.usage.output_tokens`	The number of tokens used in the model output (generation).
`gen_ai.usage.total_tokens`	The total number of tokens used in the model exchange.
`gen_ai.prompt`	The full prompt sent to the model.

Image prompt data

The image prompt data are typically too big to be included in an observation as span attributes. The preferred way to store large data it is as span events, which are supported by OpenTelemetry but not yet surfaced through the Micrometer APIs. Spring AI supports storing these fields as events in OpenTelemetry and will provide a more general event based solution once the issue github.com/micrometer-metrics/micrometer/issues/5238 is resolved.

Property

Description

Default

spring.ai.image.observations.include-prompt

true or false

false

Vector Stores

All vector store implementations in Spring AI are instrumented to provide metrics and distributed tracing data through Micrometer.

Table 11. Low Cardinality Keys
Name	Description
`spring.ai.kind`	Spring AI kind - `vector_store`
`db.system`	The database management system (DBMS) product as identified by the client instrumentation. One of `pg_vector`, `azure`, `cassandra`, `chroma`, `elasticsearch`, `milvus`, `neo4j`, `opensearch`, `qdrant`, `redis`, `typesense`, `weaviate`, `pinecone`, `oracle`, `mongodb`, `gemfire`, `hana`, `simple`
`db.operation.name`	The name of the operation or command being executed. One of `add`, `delete`, or `query`.

Table 12. High Cardinality Keys
Name	Description
`db.collection.name`	The name of a collection (table, container) within the database.
`db.vector.dimension_count`	The dimension of the vector.
`db.vector.field_name`	The name field as of the vector (e.g. a field name).
`db.vector.query.filter`	The metadata filters used in the search query.
`db.namespace`	The namespace of the database.
`db.vector.query.content`	The content of the search query being executed.
`db.vector.query.response.documents`	Returned documents from a similarity search query. Needs to be enabled with auto-configuration and use of OpenTelemetry events.
`db.vector.similarity_metric`	The metric used in similarity search.
`db.vector.query.similarity_threshold`	Similarity threshold that accepts all search scores. A threshold value of 0.0 means any similarity is accepted or disable the similarity threshold filtering. A threshold value of 1.0 means an exact match is required.
`db.vector.query.top_k`	The top-k most similar vectors returned by a query.

Vector Store response data

The Vector Store response data are typically too big to be included in an observation as span attributes. The preferred way to store large data it is as span events, which are supported by OpenTelemetry but not yet surfaced through the Micrometer APIs. Spring AI supports storing these fields as events in OpenTelemetry and will provide a more general event based solution once the issue github.com/micrometer-metrics/micrometer/issues/5238 is resolved.

Property

Description

Default

spring.ai.vectorstore.observations.include-query-response

true or false

false